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Solutions - Probability Distributions
6.3 Boston Red Sox hitting:
a) The probabilities give a legitimate probability distribution because each one is
between 0 and 1 and the sum of all of them is 1.
b)  = 0P(0) + 1P(1) + 2P(2) + 3P(3) + 4P(4) = 0(0.718) + 1(0.174) + 2(0.065) +
3(0.004) + 4(0.039) =0.472
c) The mean represents a long term average number of bases for each time at bat.
and therefore does not necessarily have to be a whole number
6.5 Bilingual Canadians?:
a) Their corresponding probabilities are not the same; that is, each x value (0, 1, and
2) does not carry the same weight.
b)  =0(0.02) + 1(0.81) + 2(0.17) = 1.15
6.15 Tail probability in graph:
The observation would fall 0.67 standard deviations above the mean, and thus, would
have a z-score of 0.67. Looking up this z-score in Table A, we see that this
corresponds to a cumulative probability of 0.749. If we subtract this from 1.0, we see
that the probability that an observation falls above this point (in the shaded region) is
0.251.
6.18 z-score for given probability in tails:
a) First we calculate the cumulative probability for the total probability of 0.02 in the
tails. We divide 0.02 by two to determine the probability in each tail, 0.01. Then
subtract the probability in the top tail from 1 to get 0.99. We can look up the
closest cumulative probabilities to 0.01 and 0.99 in Table A to get z-scores of 2.33 and 2.33.
b) The probability more than 2.33 standard deviations above the mean equals 0.01
both because it is half of 0.02, the probability that it would fall beyond this zscore in either direction, and because it is the probability above the cumulative
probability of 0.99 associated with this z-score and below the cumulative
probability of 0.01 associated with this z-score.
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c) 2.33 standard deviations above the mean is the 99 percentile because only 1%
(0.01) of the population falls above this standard deviation.
6.19 Probability in tails for given z-score:
a) We have to divide this probability by two to find the amount in each tail, 0.005.
We then subtract this from 1.0 to determine the cumulative probability associated
with this z-score, 0.995. We can look up this probability on Table A to find the zscore of 2.58.
b) For both (a) and (b), we divide the probability in half, subtract from one, and
look it up on Table A.
(a) 1.96
(b) 1.64
6.23 Blood pressure:
x
a) z 
= (140 – 121)/16 = 1.19

b) A z-score of 1.19 corresponds to a cumulative probability of 0.8830. The amount
above this z-score would be 1 – 0.8830 = 0.1170 (rounds to 0.12).
x
c) z 
= (100 – 121)/16 = - 1.31 which corresponds to a cumulative

probability of 0.0951. If we subtract 0.0951 from 0.8830 (the cumulative
probability for 140), we get 0.7879 (rounds to 0.79), the probability between 100
and 140.
6.27 MDI:
a) (i) z 
x
= (120 – 100)/16 = 1.25 which corresponds to a cumulative probability

of 0.8944. Subtracted from 1.0, this indicates that the proportion of children with
MDI of at least 120 is 0.1056.
x
(ii) z 
= (80 – 100)/16 = - 1.25 which corresponds to a cumulative probability

of 0.1056. Subtracted from 1.0, this indicates that the proportion of children with
MDI of at least 80 is 0.8944.
th
b) The z-score corresponding to the 99 percentile is 2.33. To find the value of x, we
calculate x = μ+ zσ = 100 + (2.33)(16) = 137.28.
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c) The z-score corresponding to the 1 percentile is - 2.33. To find the value of x, we
calculate x = μ+ zσ = 100 + (- 2.33)(16) = 62.72.
6.36 Number of girls in family:
a) The data are binary (boy, girl), there is the same probability of success for each
trial (0.49), and the trials are independent (a previous child’s sex has no bearing
on the next child’s sex).
b) For this distribution, n is four, and p is 0.49.
c) The probability that the family has 2 girls and 2 boys is:
4!
.49 2 1  .49 4  2 = (24/4)[(0.2401)(0.2601)] = 0.3747
P2 
2! 4  2!
(rounds to 0.37)
6.43 Jury Duty:
a) One can assume that X has a binomial distribution because 1) the data are binary
(Hispanic or not), 2) there is the same probability of success for each trial (i.e.,
0.40), and 3) each trial is independent of the other trials (whom you pick for the
first juror is not likely to affect whom you pick for the other jurors, and n < 10%
of the population size). n = 12; p = 0.40.
b) The probability that no Hispanic is selected is:
12!
.400 1  .40120  = (1)(1)(0.002177) = 0.002177 (rounds to
P0 
0!12  0!
0.0022)
c) If no Hispanic is selected out of a sample of size 12, this does cast doubt on
whether the sampling was truly random. There is only a 0.22 % chance that this
would occur if the selection were done randomly.